Recent developments in medical image analysis techniques make them essential tools in medical diagnosis. Medical imaging is always involved with different kinds of uncertainties. Managing these uncertainties has motivated extensive research on medical image classification methods, particularly for the past decade. Despite being a powerful classification tool, the sparse representation suffers from the lack of sufficient discrimination and robustness, which are required to manage the uncertainty and noisiness in medical image classification issues. It is tried to overcome this deficiency by introducing a new fuzzy discriminative robust sparse representation classifier, which benefits from the fuzzy terms in its optimization function of the dictionary learning process. In this work, we present a new medical image classification approach, fuzzy discriminative sparse representation (FDSR). The proposed fuzzy terms increase the inter-class representation difference and the intra-class representation similarity. Also, an adaptive fuzzy dictionary learning approach is used to learn dictionary atoms. FDSR is applied on Magnetic Resonance Images (MRI) from three medical image databases. The comprehensive experimental results clearly show that our approach outperforms its series of rival techniques in terms of accuracy, sensitivity, specificity, and convergence speed.

医学图像分析技术的最新发展使其成为医学诊断中必不可少的工具。医学成像总是涉及不同种类的不确定性。管理这些不确定性激发了对医学图像分类方法的广泛研究，特别是在过去十年中。尽管是一种强大的分类工具，但稀疏表示缺乏足够的辨别力和鲁棒性，这是管理医学图像分类问题中的不确定性和噪声所必需的。它试图通过引入一种新的模糊判别鲁棒稀疏表示分类器来克服这一缺陷，该分类器受益于字典学习过程的优化功能中的模糊项。在这项工作中，我们提出了一种新的医学图像分类方法，模糊判别稀疏表示（FDSR）。提出的模糊项增加了类间表示差异和类内表示相似度。此外，自适应模糊字典学习方法用于学习字典原子。FDSR 应用于来自三个医学图像数据库的磁共振图像 (MRI)。综合实验结果清楚地表明，我们的方法在准确性、灵敏度、特异性和收敛速度方面优于其一系列竞争技术。FDSR 应用于来自三个医学图像数据库的磁共振图像 (MRI)。综合实验结果清楚地表明，我们的方法在准确性、灵敏度、特异性和收敛速度方面优于其一系列竞争技术。FDSR 应用于来自三个医学图像数据库的磁共振图像 (MRI)。综合实验结果清楚地表明，我们的方法在准确性、灵敏度、特异性和收敛速度方面优于其一系列竞争技术。